Much like a linear model, logistic regression is a flexible framework used to investigate the relationship between multiple variables, and a single outcome. The explanatory variables can be continuous or categorical, while the outcome must be a dichotomous variable (i.e. having only two unique values).
This dialog is used to define what variables are of interest, and how they should be treated.
as.numericfunction, so make sure that the order of the factor levels is correct.
If your specified outcome variable is continuous, or has multiple levels, you may choose how you wish to dichotomize it by clicking on the "split" button. This will bring up a dialog in which you can define your dichotomization.
R has a rich syntax for expressing model formulae. The model builder dialog assists in the specification of the terms of a linear regression model.
Only one outcome is allowed. It can be edited by double clicking on it.
Select one or more variables from the Variable list, the click on one of the center buttons to add a term to the model.
Additionally, terms can be hand edited by double clicking on them.
After the model has been built, its features can be explored. The preview panel displays a preview of what will be displayed in the console when the model is run. In the upper left hand portion of the dialog there are icons representing the assumptions that are being made by the model.
The following analysis options and dialogs are available
Additionally, several plots can be accessed through the upper tabs to help diagnose the fit of the model.
The diagnostics panel contains 4 plots evaluating outliers, influence, and equality of variance.
Cook's distance Outliers can unduly influence the results of the model. This plot shows the row names for observations with moderate or high cook's distance. If the Cook's value is greater than 1, the observation should be examined.
If the model contains no interactions, component residual plots are given. These are used to assess the linearity of the relationship between predictor variables and the outcome.